6.2 Simple Python Experiments with LLMs
One of the most exciting aspects of today’s LLM ecosystem is its accessibility. With just Python and a few packages, you can begin running powerful natural language processing tasks—locally or in the cloud—without being a machine learning expert.
In Chapter 6.2 of the book, I share representative Python examples using Hugging Face, OpenAI, Google Cloud, and Azure. These hands-on snippets demonstrate how easy it is to generate text, analyze sentiment, and build applications powered by LLMs.
What You’ll Discover in This Chapter
1. Hugging Face Transformers
Generate text locally (or on GPU) with models like Mistral-7B using just a few lines of code. Perfect for developers seeking control and transparency.
2. OpenAI API
Call GPT-4 and other hosted models via REST to build chatbots, summarizers, or content generators—no downloads or infrastructure required.
3. Google Cloud Natural Language
Send text to the cloud and receive structured sentiment scores or entity extraction results. Ideal for analyzing customer feedback or social data at scale.
4. Azure Cognitive Services
Access enterprise-ready NLP endpoints for sentiment, key phrases, and language detection. Designed with compliance and SLAs for regulated environments.
6.2 covers:
- You don’t need deep ML expertise to start building with LLMs.
- Hugging Face enables local control and experimentation with open-source models.
- OpenAI offers fast access to cutting-edge hosted models like GPT-4.
- Google Cloud simplifies large-scale sentiment analysis and entity extraction.
- Azure provides a robust, enterprise-ready NLP suite for critical applications.
This article is adapted from the book “A Guide to LLMs (Large Language Models): Understanding the Foundations of Generative AI.” The full version—with complete explanations, and examples—is available on Amazon Kindle or in print.
You can also browse the full index of topics online here: LLM Tutorial – Introduction, Basics, and Applications .
SHO
CTO of Receipt Roller Inc., he builds innovative AI solutions and writes to make large language models more understandable, sharing both practical uses and behind-the-scenes insights.Category
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SHO
CTO of Receipt Roller Inc., he builds innovative AI solutions and writes to make large language models more understandable, sharing both practical uses and behind-the-scenes insights.